To develop an intelligent delivery time prediction system using neural networks that enhances customer satisfaction and operational efficiency by providing accurate, real-time ETAs for food deliveries across Porter’s intra-city logistics network.
-You can access the complete project python file here - Python
-You can access the complete project in pdf format here - Report
| Feature | Description |
|---|---|
| market_id | integer id for the market where the restaurant lies |
| created_at | the timestamp at which the order was placed |
| actual_delivery_time | the timestamp when the order was delivered |
| store_primary_category | category for the restaurant |
| order_protocol | integer code value for order protocol |
| total_items subtotal | final price of the order |
| num_distinct_items | the number of distinct items in the order |
| min_item_price | price of the cheapest item in the order |
| max_item_price | price of the costliest item in order |
| total_onshift_partners | number of delivery partners on duty at the time order was placed |
| total_busy_partners | number of delivery partners attending to other tasks |
| total_outstanding_orders | total number of orders to be fulfilled at the moment |
| estimated_store_to_consumer_driving_duration | approximate travel time from restaurant to customer |
-Neural Network model have performed well with a good performance metrics. -These models could be used to predict delivery time of orders in a future percpective. -From the feature correlation, total outstanding orders and total busy dashers are found to be most important is deciding the delivery time of orders from Porter.